Abstract
Nowadays, the analysis of vehicular ad hoc networks for the evaluation of traffic conditions is a hot research field. One of the most significant process in VANETs is the vehicle clusterization. Indeed, in order to optimize the information exchange in such a network, an opportune criterion to aggregate vehicles is needed. The main goal of this work is to propose an innovative strategy to perform such an aggregation, by not considering only the punctual position of vehicles in a given instant, but by taking into account the behavior of vehicles over the time, so that a cluster contains vehicles that behave the same. To this aim, we propose a model that processes information about nodes of the network, and extracts a similarity measure between pair of vehicles to establish if they should belong to the same cluster.
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Balzano, W., Murano, A., Sorrentino, L., Stranieri, S. (2020). Behavioral Clustering: A New Approach for Traffic Congestion Evaluation. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Web, Artificial Intelligence and Network Applications. WAINA 2020. Advances in Intelligent Systems and Computing, vol 1150. Springer, Cham. https://doi.org/10.1007/978-3-030-44038-1_129
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DOI: https://doi.org/10.1007/978-3-030-44038-1_129
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